Exploratory Data Analysis (EDA) Using Seaborn

Evaluate your EDA and visualization skills.

1. What is the primary goal of Exploratory Data Analysis (EDA)?
2. Which Seaborn functions are used to visualize relationships between two numerical variables?
3. Seaborn's `pairplot()` generates scatterplots for all numerical variable pairs and histograms/kde plots for univariate distributions.
4. Name the Seaborn function commonly used to visualize a correlation matrix (one word).
5. Which Seaborn plot is best for comparing the distribution of a numerical variable across categories?
6. Which parameters can add additional categorical variables to a Seaborn plot?
7. Seaborn is built on top of Matplotlib.
8. Which Seaborn function visualizes the univariate distribution of a numerical variable?
9. Which Seaborn parameter colors plot elements based on a categorical variable?
10. Which Seaborn object creates a grid of plots based on categorical variables (rows/columns)?
11. Which are examples of univariate plots in Seaborn?
12. Seaborn plots can be customized using Matplotlib functions (e.g., plt.title()).
13. What is the purpose of `sns.set_style()`?
14. Name the built-in Seaborn dataset containing restaurant tipping data (lowercase).
15. Which are categorical plots in Seaborn?
16. `sns.countplot()` displays the number of observations in each category of a categorical variable.
17. Which parameter specifies the y-axis variable in most Seaborn plots?
18. What does 'KDE' stand for in Seaborn's distribution plots?
19. Which are Seaborn figure-level functions?
20. Which Seaborn function generates a scatterplot matrix for numerical variables in a dataset?
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